How to Perform Object Detection in Photographs Using Mask R-CNN with Keras

Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph.

It is a challenging problem that involves building upon methods for object recognition (e.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are they).

In recent years, deep learning techniques have achieved state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions. Most notably is the R-CNN, or Region-Based Convolutional Neural Networks, and the most recent technique called Mask R-CNN that is capable of achieving state-of-the-art results on a range of object detection tasks.

In this tutorial, you will discover how to use the Mask R-CNN model to detect objects in new photographs.

After completing this tutorial, you will know:

The region-based Convolutional Neural Network family of models for object detection and the most recent variation called Mask R-CNN.

The best-of-breed open source library implementation of the Mask R-CNN for the Keras deep learning library.

How to use a pre-trained Mask R-CNN to perform object localization and detection on new photographs.

Let’s get started.

How to Perform Object Detection in Photographs With Mask R-CNN in KerasPhoto by Ole Husby, some rights reserved.

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Mask R-CNN for Object Detection

Object detection is a computer vision task that involves both localizing one or more objects within an image and classifying each object in the image.

It is a challenging computer vision task that requires both successful object localization in order to locate and draw a bounding box around each object in an image, and object classification to predict the correct class of object that was localized.

An extension of object detection involves marking the specific pixels in the image that belong to each detected object instead of using coarse bounding boxes during object localization. This harder version of the problem is generally referred to as object segmentation or semantic segmentation.

The Region-Based Convolutional Neural Network, or R-CNN, is a family of convolutional neural network models designed for object detection, developed by Ross Girshick, et al.

There are perhaps four main variations of the approach, resulting in the current pinnacle called Mask R-CNN. The salient aspects of each variation can be summarized as follows:

R-CNN: Bounding boxes are proposed by the “selective search” algorithm, each of which is stretched and features are extracted via a deep convolutional neural network, such as AlexNet, before a final set of object classifications are made with linear SVMs.

Fast R-CNN: Simplified design with a single model, bounding boxes are still specified as input, but a region-of-interest pooling layer is used after the deep CNN to consolidate regions and the model predicts both class labels and regions of interest directly.

Faster R-CNN: Addition of a Region Proposal Network that interprets features extracted from the deep CNN and learns to propose regions-of-interest directly.

Mask R-CNN: Extension of Faster R-CNN that adds an output model for predicting a mask for each detected object.

The Mask R-CNN model introduced in the 2018 paper titled “Mask R-CNN” is the most recent variation of the family models and supports both object detection and object segmentation. The paper provides a nice summary of the model linage to that point:

The Region-based CNN (R-CNN) approach to bounding-box object detection is to attend to a manageable number of candidate object regions and evaluate convolutional networks independently on each RoI. R-CNN was extended to allow attending to RoIs on feature maps using RoIPool, leading to fast speed and better accuracy. Faster R-CNN advanced this stream by learning the attention mechanism with a Region Proposal Network (RPN). Faster R-CNN is flexible and robust to many follow-up improvements, and is the current leading framework in several benchmarks.

The family of methods may be among the most effective for object detection, achieving then state-of-the-art results on computer vision benchmark datasets. Although accurate, the models can be slow when making a prediction as compared to alternate models such as YOLO that may be less accurate but are designed for real-time prediction.

Matterport Mask R-CNN Project

Mask R-CNN is a sophisticated model to implement, especially as compared to a simple or even state-of-the-art deep convolutional neural network model.

Source code is available for each version of the R-CNN model, provided in separate GitHub repositories with prototype models based on the Caffe deep learning framework. For example:

Instead of developing an implementation of the R-CNN or Mask R-CNN model from scratch, we can use a reliable third-party implementation built on top of the Keras deep learning framework.

The best of breed third-party implementations of Mask R-CNN is the Mask R-CNN Project developed by Matterport. The project is open source released under a permissive license (i.e. MIT license) and the code has been widely used on a variety of projects and Kaggle competitions.

Nevertheless, it is an open source project, subject to the whims of the project developers. As such, I have a fork of the project available, just in case there are major changes to the API in the future.

The project is light on API documentation, although it does provide a number of examples in the form of Python Notebooks that you can use to understand how to use the library by example. Two notebooks that may be helpful to review are:

Mask R-CNN Installation

The first step is to install the library.

At the time of writing, there is no distributed version of the library, so we have to install it manually. The good news is that this is very easy.

Installation involves cloning the GitHub repository and running the installation script on your workstation. If you are having trouble, see the installation instructions buried in the library’s readme file.

Step 1. Clone the Mask R-CNN GitHub Repository

This is as simple as running the following command from your command line:

git clone https://github.com/matterport/Mask_RCNN.git

This will create a new local directory with the name Mask_RCNN that looks as follows:

We will define the model as type “inference” indicating that we are interested in making predictions and not training. We must also specify a directory where any log messages could be written, which in this case will be the current working directory.

Running the example loads the model and performs object detection. More accurately, we have performed object localization, only drawing bounding boxes around detected objects.

In this case, we can see that the model has correctly located the single object in the photo, the elephant, and drawn a red box around it.

Photograph of an Elephant With All Objects Localized With a Bounding Box

Example of Object Detection

Now that we know how to load the model and use it to make a prediction, let’s update the example to perform real object detection.

That is, in addition to localizing objects, we want to know what they are.

The Mask_RCNN API provides a function called display_instances() that will take the array of pixel values for the loaded image and the aspects of the prediction dictionary, such as the bounding boxes, scores, and class labels, and will plot the photo with all of these annotations.

One of the arguments is the list of predicted class identifiers available in the ‘class_ids‘ key of the dictionary. The function also needs a mapping of ids to class labels. The pre-trained model was fit with a dataset that had 80 (81 including background) class labels, helpfully provided as a list in the Mask R-CNN Demo, Notebook Tutorial, listed below.